Underwater Image Processing with New Dark Channel Prior Dehazing
Ruikang Hu, Yuhan Li · 2023
Aiming at addressing the problems of low visibility and poor contrast, this paper proposes a new dark channel prior dehazing. According to the characteristics of the light source, an image is divided into light and non-light source areas. Mixed precision operation is used to subsample the dark channel image and deep learning network and GPU-accelerated method are used to improve the algorithm speed to solve the real-time problem. Experimental results show that compared with similar algorithms, the new algorithm is more balanced in image quality indicators and underwater image indicators, which better working requirements of underwater vehicles. In terms of real-time performance, the new algorithm is superior to similar algorithms. When processing images a$950\times 550$pixel, resolving new with an average frame rate of 29.4, which runs 2.46 times faster than dark channel prior, which lays a foundation for underwater robots to carry out underwater operations more efficiently.